In a community question-answering (CQA) network such as Yahoo!Answers, it is desirable to present users with questions that match their interests in order to foster a better community experience. Many users, however, are reluctant to answer formal questionnaires that indicate their preferences. Modern systems resort to automatically inferring user preferences from the user's historical interaction with the system. Unfortunately, these methods are not applicable to new users, with no history within the system. Yet it is critical to retain the new users while they visit the CQA network for the first time because they represent a great opportunity for new traffic.
Therefore there is a need for a learning method that overcomes the above-stated shortcomings of the known art.